A system includes a processor receiving spectrometer data representative of a scanned sample and generated by a spectrometer and a cloud server including a server processor. The server processor receives the spectrometer data generated by the spectrometer from the processor, analyzes the spectrometer data, identifies, based on a machine learning application, one or more unique characteristics of the spectrometer data which uniquely identifies the scanned sample and provides to the processor data representative of a graphical display, which includes an indication of whether or not the scanned sample includes the one or more unique characteristics of the spectrometer data.

A method may include drilling a wellbore, the wellbore intersecting a shale formation at an interval of the shale formation and casing at least a portion of the wellbore. The method may also include perforating the casing at the interval to fluidly couple the interval and the wellbore, and liberating free and absorbed gas entrapped within the interval. In addition, the method may include solubilizing in the wellbore fluid the free and absorbed gas, forming a plume comprising solubilized gas, and determining an identity and amount of solubilized gas in the plume.

A method of predicting a parameter of a medium to be observed in a bioprocess based on Raman spectroscopy including the steps of acquiring a first series of preparatory Raman spectra of an aqueous medium using a first measuring assembly; normalizing the first series of preparatory Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the first measuring assembly; building a multivariate model for the parameter based on the normalized preparatory Raman spectra; acquiring predictive Raman spectra of the medium to be observed during the bioprocess with another measuring assembly; normalizing the predictive Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the other measuring assembly; and applying the built model to the predictive Raman spectra for predicting the parameter.

Provided is a urea solution sensor that can accurately measure a concentration of urea. The ammonia concentration sensor (1) includes: a light source (10) that emits measurement light toward a measurement subject, the measurement light including near-infrared light; a light reception unit (20) that receives transmitted light or reflected light from the measurement subject; and an analysis unit (30) that analyzes a concentration of urea contained in the measurement subject based on a spectrum of light which has been received by the light reception unit (20).

Systems and methods for performing a contamination estimation of a downhole sample including at least a formation fluid and a filtrate are provided. A plurality of downhole signals are obtained from the downhole sample and one or more of the signals are conditioned. At least two of the conditioned signals or downhole signals are fused into a multivariate dataset. A principle component analysis (PCA) is performed on the fused multivariate dataset to determine optical and density properties of the formation fluid. Based on at least the PCA, optical and density properties of the filtrate are determined. From the optical and density properties of the formation fluid and of the filtrate, a multivariate calculation is performed to generate concentration profiles of the formation fluid and the filtrate.

A system for non-invasively measuring an analyte in a vehicle driver and controlling a vehicle based on a measurement of the analyte. At least one solid-state light source is configured to emit different wavelengths of light. A sample device is configured to introduce the light emitted by the at least one solid-state light source into tissue of the vehicle driver. One or more optical detectors are configured to detect a portion of the light that is not absorbed by the tissue of the vehicle driver. A controller is configured to calculate a measurement of the analyte in the tissue of the vehicle driver based on the light detected by the one or more optical detectors, determine whether the measurement of the analyte in the tissue of the vehicle driver exceeds a pre-determined value, and provide a signal to a device configured to control the vehicle.

According to one embodiment, there is provided a measuring apparatus including a measurement section and a control section. The measurement section is configured to acquire a response from a sample. The control section is configured to compare a loading obtained by performing principal component analysis in advance with a first evaluation-use loading obtained by performing principal component analysis onto the response acquired from the sample, and to generate a first reliability index for measurement using principal component analysis, in accordance with a comparison result.

A surface plasmonic sensing device (10) comprises a substrate (12) and a first array (20) and a second array (22) of localised surface plasmon resonance island structures (20, 22) on the substrate (12). The surface plasmon resonance island structures (20, 22) of the first (20) and second (22) array respectively have first and second surface functionalisation for selective interaction with respective analytes. The first surface functionalisation is different to the second surface functionalisation. The first (20) and second (22) arrays are interspersed with each other to provide a composite array in a main sensing region (14) of the device (10). Also disclosed is a method for manufacturing a surface plasmonic sensing device (10) and a method of analysing a fluid comprising a mixture of two or more analytes. The surface plasmonic sensing device (10) may further comprise a reference region (16) and an auxiliary sensing region (18).

Method to identify microorganisms in a sample, by evaluating the vibrational profile.

Disclosed is a method for quantification of water and/or one or more ionic liquid components in an ionic liquid (IL)/water (H2O) mixture. The method includes obtaining one or more Raman spectra for the IL/H2O mixture, and using a quantitative calibration model with the one or more Raman spectra to quantify water and/or one or more ionic liquid components in the IL/H2O mixture.